1.1k citations · 1.3k across the 15 of their papers we have counts for
20 papers
A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging
Antony Jerald, Hemant K Aggarwal, Brian Nett +4
Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this wo…
Display Field-Of-View Agnostic Robust CT Kernel Synthesis Using Model-Based Deep Learning
Hemant Kumar Aggarwal, Antony Jerald, Phaneendra K. Yalavarthy +2
In X-ray computed tomography (CT) imaging, the choice of reconstruction kernel is crucial as it significantly impacts the quality of clinical images. Different kernels influence sp…
LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
Hemant Kumar Aggarwal, Sudhanya Chatterjee, Dattesh Shanbhag +2
Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition t…
Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
Maneesh John, Hemant Kumar Aggarwal, Qing Zou +1
Deep learning algorithms that rely on extensive training data are revolutionizing image recovery from ill-posed measurements. Training data is scarce in many imaging applications,…
Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimator
Hemant Kumar Aggarwal, Mathews Jacob
Deep learning image reconstruction algorithms often suffer from model mismatches when the acquisition scheme differs significantly from the forward model used during training. We i…
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
Hemant Kumar Aggarwal, Aniket Pramanik, Maneesh John +1
Image reconstruction using deep learning algorithms offers improved reconstruction quality and lower reconstruction time than classical compressed sensing and model-based algorithm…
Model-Based Deep Learning for Reconstruction of Joint k-q Under-sampled High Resolution Diffusion MRI
Merry P. Mani, Hemant K. Aggarwal, Sanjay Ghosh +1
We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high resolution imaging. Th…
Label Consistent Transform Learning for Hyperspectral Image Classification
Jyoti Maggu, Hemant K. Aggarwal, Angshul Majumdar
This work proposes a new image analysis tool called Label Consistent Transform Learning (LCTL). Transform learning is a recent unsupervised representation learning approach; we add…
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification
Vanika Singhal, Hemant K. Aggarwal, Snigdha Tariyal +1
This work proposes a new framework for deep learning that has been particularly tailored for hyperspectral image classification. We learn multiple levels of dictionaries in a robus…
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach
Angshul Majumdar, Naushad Ansari, Hemant Aggarwal +1
In this work we propose a technique to remove sparse impulse noise from hyperspectral images. Our algorithm accounts for the spatial redundancy and spectral correlation of such ima…
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstr…
Dynamic MRI using deep manifold self-learning
Abdul Haseeb Ahmed, Hemant Aggarwal, Prashant Nagpal +1
We propose a deep self-learning algorithm to learn the manifold structure of free-breathing and ungated cardiac data and to recover the cardiac CINE MRI from highly undersampled me…
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (S…
J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
Hemant Kumar Aggarwal, Mathews Jacob
Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce…
Improved Reconstruction for high-resolution Multi-shot Diffusion Weighted Imaging
Merry Mani, Hemant Kumar Aggarwal, Vincent Magnotta +1
Purpose: To introduce a fast and improved direct reconstruction method for multi-shot diffusion weighted (msDW) scans for high-resolution studies. Methods:Multi-shot EPI methods ca…
Off-the-grid model based deep learning (O-MODL)
Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies i…
MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI
Hemant Kumar Aggarwal, Merry P. Mani, Mathews Jacob
We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. The proposed al…
Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar +1
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) car…
MoDL: Model Based Deep Learning Architecture for Inverse Problems
Hemant Kumar Aggarwal, Merry P. Mani, Mathews Jacob
We introduce a model-based image reconstruction framework with a convolution neural network (CNN) based regularization prior. The proposed formulation provides a systematic approac…
Extension of Sparse Randomized Kaczmarz Algorithm for Multiple Measurement Vectors
Hemant Kumar Aggarwal, Angshul Majumdar
The Kaczmarz algorithm is popular for iteratively solving an overdetermined system of linear equations. The traditional Kaczmarz algorithm can approximate the solution in few sweep…